Beam-Based Mobility Management in 5G Millimetre Wave V2X Communications: A Survey and Outlook

Kose, Abdulkadir; Lee, Haeyoung; Foh, Chuan Heng; Dianati, Mehrdad

doi:10.1109/ojits.2021.3112533

Cited by 22 publications

(10 citation statements)

References 56 publications

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“…This forms a more complex network of relationships. If you rely on 5G R&D personnel to manually handle these complex correlation issues and complex data relationships, it will not only cause huge waste of human resources and financial resources [9,10]. There is also lower efficiency in this way.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Allocation Method of 5G Communication System Resources Assisted by Artificial Intelligence Technology

Liu

2022

Wireless Communications and Mobile Computing

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5G technology has been rapidly developed under the trend of utilization in various countries. It is an information dissemination technology with faster and more stable transmission speed. Compared with 4G technology, 5G technology has a low delay rate and higher transmission speed, and it can be applied in tasks such as the Internet of Vehicles and the transmission of 3D images. For 5G technology, the communication system is a huge challenge. It not only needs to deal with the transmission speed between users and users but also needs to deal with the efficiency between users and channels. 5G researchers will spend a lot of material and financial resources to solve these problems. This study uses artificial intelligence technology to analyze its application in resource allocation of 5G communication systems. It also uses the collaborative filtering algorithm to realize the automatic recommendation and matching problem of 5G communication system resources. The research results show that artificial intelligence technology has good feasibility in the resource allocation problem of 5G communication system. It can achieve the resource allocation task of 5G technology communication system with high efficiency and accuracy, and all prediction errors are within 3% for the characteristics of the three 5G communication systems in this study.

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Section: Introductionmentioning

confidence: 99%

“…Equations ( 9) and ( 10) correspond to Equations ( 4) and ( 5). Since the CNN will contain many hidden layers, Equations ( 9) and (10) show the expansion of the weights and biases in the hidden layers. It also shows an automatic differentiation method.…”

Section: Introduction To Cnn and Collaborative Filteringmentioning

confidence: 99%

Optimal Allocation Method of 5G Communication System Resources Assisted by Artificial Intelligence Technology

Liu

2022

Wireless Communications and Mobile Computing

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“…(3) High training overhead. As described in the 3rd Generation Partnership Project (3GPP), the array sizes of macro Base Station (BS) are up to 256 elements while the cellular User Equipment (UE) has 32 elements in the new Vehicle-to-Everything (V2X) use cases [16], [17]. A large scale antenna array will cause high training overhead inevitably.…”

Section: Introductionmentioning

confidence: 99%

Beam Training and Tracking in MmWave Communication: A Survey

Wang¹,

Wei²,

Zhang³

2022

Preprint

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Communicating on millimeter wave (mmWave) bands is ushering in a new epoch of mobile communication which provides the availability of 10 Gbps high data rate transmission. However, mmWave links are easily prone to short transmission range communication because of the serious free space path loss and the blockage by obstacles. To overcome these challenges, highly directional beams are exploited to achieve robust links by hybrid beamforming. Accurately aligning the transmitter and receiver beams, i.e. beam training, is vitally important to high data rate transmission. However, it may cause huge overhead which has negative effects on initial access, handover, and tracking. Besides, the mobility patterns of users are complicated and dynamic, which may cause tracking error and large tracking latency. An efficient beam tracking method has a positive effect on sustaining robust links. This article provides an overview of the beam training and tracking technologies on mmWave bands and reveals the insights for future research in the 6th Generation (6G) mobile network. Especially, some open research problems are proposed to realize fast, accurate, and robust beam training and tracking. We hope that this survey provides guidelines for the researchers in the area of mmWave communications.

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“…However, the mmWave channel faces several challenges, such as high mobility, blockages, high transmission loss, etc. To compensate for high transmission loss, mmWave transmitters can utilize directional transmissions, often referred to as a mmWave beam [12]. Mobility management is another prominent issue with direct transmission, especially for mmWave-based vehicular communication.…”

Section: Introductionmentioning

confidence: 99%

“…Mobility management is another prominent issue with direct transmission, especially for mmWave-based vehicular communication. As per Release 16 of the third generation partnership project (3GPP), vehicles face different types of mobility with mmWave transmission, e.g., beam level and cell level mobility [12]. Still, there are many open issues that need to be resolved before implementing mmWave-based transmission in practical vehicular communication scenarios.…”

Section: Introductionmentioning

confidence: 99%

On the Performance Analysis of V2N Mixed RF and Hybrid FSO/RF Communication System

et al. 2022

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Vehicular communications allow vehicles to connect with other vehicles and network infrastructures in order to facilitate the transfer of real-time information and dependable transportation. This paper proposes a system that connects vehicles to the network (base station) in two phases using transceiversequipped roadside infrastructures, such as signboards, traffic lights, and street lights, as intermediary relay nodes. In the first phase, the information transfer occurs from vehicles to infrastructure using a radio-frequency (RF) link. The second phase includes establishing communication from the infrastructure to the base station using hybrid free-space optics (FSO)/RF link. In modeling the FSO link, we consider factors like atmospheric attenuation, pointing errors, and atmospheric turbulence-induced fading, which can affect FSO performance. For this set-up, we derive the accurate expressions for the outage probability, system throughput, average symbol error rate, and average end-toend delay. Numerical results corroborate the dependency of the time allocation factor α for the vehicle-to-infrastructure link on the vehicle transmit power. Furthermore, the results elucidate the impact of the length of the transmitted packet and FSO link distance on average end-to-end delay performance. The increase in distance between infrastructure and base station can be compensated by reducing the length of packets to achieve desirable delay performance.

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Beam-Based Mobility Management in 5G Millimetre Wave V2X Communications: A Survey and Outlook

Cited by 22 publications

References 56 publications

Optimal Allocation Method of 5G Communication System Resources Assisted by Artificial Intelligence Technology

Optimal Allocation Method of 5G Communication System Resources Assisted by Artificial Intelligence Technology

Beam Training and Tracking in MmWave Communication: A Survey

On the Performance Analysis of V2N Mixed RF and Hybrid FSO/RF Communication System

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